Protection of athletes’ rights and interests: Application of biomechanics in sports law and legal issues
Abstract
With the continuous refinement of sports law, the scientific safeguarding of athletes’ rights to fair competition and their physical and mental well-being has emerged as a critical issue. This study introduces an innovative approach that integrates biomechanical technologies with a multidimensional cloud model algorithm, applied within the realm of sports law. Its primary objective is to optimize refereeing decisions and prevent sports injuries through objective data analysis. The research employs track and field events as its experimental setting, where athlete data—including joint angular displacement, angular velocity, angular acceleration, and force measurements—are captured using the Mediapipe motion capture system in conjunction with force platform technology. A multidimensional cloud model is then employed to conduct both quantitative and qualitative analyses of infractions (such as false starts) and fatigue states. Results indicate that, compared to traditional refereeing, this method not only achieves higher accuracy in identifying false starts but also effectively detects fatigue through joint motion parameters, thereby providing a robust scientific basis for athlete health monitoring. Furthermore, the study examines legal risks associated with the application of biomechanical data, such as issues related to data privacy, ownership, and evidentiary validity, and proposes compliance measures including informed consent, data encryption, and standardized protocols. Empirical findings demonstrate that biomechanical technology can significantly enhance the objectivity of adjudication, reduce misjudgment rates, and support the management of athletes’ training loads—thus playing a pivotal role in maintaining fair competition and safeguarding athletes’ well-being. Future research should further integrate intelligent algorithms and expand sample diversity to promote a deeper convergence between biomechanics and sports law.
References
1. Vetter RE, Symonds ML. Correlations Between Injury, Training Intensity, and Physical and Mental Exhaustion Among College Athletes. Journal of Strength and Conditioning Research. 2010; 24(3): 587-596. doi: 10.1519/jsc.0b013e3181c7c2eb
2. Mitten MJ, Davis T. Athlete eligibility requirements and legal protection of sports participation opportunities. Virginia Sports and Entertainment Law Journal. 2008.
3. Li M. Research on the application of biomechanics analysis in optimizing physical education movement techniques. Molecular & Cellular Biomechanics. 2024; 21(3): 496. doi: 10.62617/mcb496
4. Ross GB. The Objective Assessment of Movement Quality Using Motion Capture and Machine Learning. Université d’Ottawa/University of Ottawa; 2022.
5. Apriantono T, Nunome H, Ikegami Y, et al. The effect of muscle fatigue on instep kicking kinetics and kinematics in association football. Journal of Sports Sciences. 2006; 24(9): 951-960. doi: 10.1080/02640410500386050
6. Tian C, Wang Y, Zhang D. Comprehensive assessment of lower limb alignment and forces during dance landings under fatigue. Molecular & Cellular Biomechanics. 2024; 21(4): 531. doi: 10.62617/mcb531
7. Irmischer BS, Harris C, Pfeiffer RP, et al. Effects of a knee ligament injury prevention exercise program on impact forces in women. Journal of Strength and Conditioning Research. 2004; 18(4): 703-707. doi: 10.1519/00124278-200411000-00003
8. Al-Ayyad M, Owida HA, De Fazio R, et al. Electromyography Monitoring Systems in Rehabilitation: A Review of Clinical Applications, Wearable Devices and Signal Acquisition Methodologies. Electronics. 2023; 12(7): 1520. doi: 10.3390/electronics12071520
9. Picerno P, Iosa M, D’Souza C, et al. Wearable inertial sensors for human movement analysis: a five-year update. Expert Review of Medical Devices. 2021; 18(sup1): 79-94. doi: 10.1080/17434440.2021.1988849
10. Chang BJ, Manton JD, Sapoznik E, et al. Real-time multi-angle projection imaging of biological dynamics. Nature Methods. 2021; 18(7): 829-834. doi: 10.1038/s41592-021-01175-7
11. Van Cutsem J, Marcora S, De Pauw K, et al. The Effects of Mental Fatigue on Physical Performance: A Systematic Review. Sports Medicine. 2017; 47(8): 1569-1588. doi: 10.1007/s40279-016-0672-0
12. Zadeh A, Taylor D, Bertsos M, et al. Predicting Sports Injuries with Wearable Technology and Data Analysis. Information Systems Frontiers. 2020; 23(4): 1023-1037. doi: 10.1007/s10796-020-10018-3
13. Fan C, Zhong L. The call of rights: A review of the research on citizens’ sports rights since the promulgation of the Sports Law of the People’s Republic of my country. China Sports Science and Technology. 2008; 44(3): 77-82.
14. Yaroshenko O, Kaganovska T, Sheverdina V, et al. Protection of Athletes’ Rights in International Sports Organizations. Statute Law Review. 2024; 45(2). doi: 10.1093/slr/hmae041
15. Li D, Liu C, Gan W. A new cognitive model: Cloud model. International Journal of Intelligent Systems. 2009; 24(3): 357-375. doi: 10.1002/int.20340
16. Ye Q, Li S, Zhang Y, et al. A review of cloud modeling and applications. Computer Engineering and Design. 2011; 32(12): 4198-4201.
17. Deng Y, Liu S, Zhang W, et al. General multidimensional cloud model and its application on spatial clustering in Zhanjiang, Guangdong. Journal of Geographical Sciences. 2010; 20(5): 787-798. doi: 10.1007/s11442-010-0811-8
18. Yao J, Wang G, Xue B, et al. Assessment of lake eutrophication using a novel multidimensional similarity cloud model. Journal of Environmental Management. 2019; 248: 109259. doi: 10.1016/j.jenvman.2019.109259
19. Wang M, Liu Q, Wang X, et al. Prediction of rockburst based on multidimensional connection cloud model and set pair analysis. International Journal of Geomechanics. 2020; 20(1). doi: 10.1061/(ASCE)GM.1943-5622.0001546
20. Liu Y, Zhan W, Li Y, et al. Grid-Related Fine Action Segmentation Based on an STCNN-MCM Joint Algorithm during Smart Grid Training. Energies. 2023; 16(3): 1455. doi: 10.3390/en16031455
21. Wang H. A study of the effect of fatigue state on soccer players’ shooting movements based on Mediapipe. Molecular & Cellular Biomechanics. 2025; 22(2): 1361. doi: 10.62617/mcb1361
22. Lugaresi C, Tang J, Nash H, et al. Mediapipe: A framework for building perception pipelines. arXiv; 2019.
23. de Gusmao Lafayette TB, Burle A de Q, Almeida A de A, et al. The Virtual Kinect. In: Proceedings of the 23rd symposium on virtual and augmented reality; 2021.
24. Hsia CH, Chien CH, Hsu HW, et al. Analyses of basketball player field goal shooting postures for player motion correction using kinect sensor. In: Proceedings of the 2014 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS); 2014.
25. Wang G, Xu C, Li D. Generic normal cloud model. Information Sciences. 2014; 280: 1-15. doi: 10.1016/j.ins.2014.04.051
26. Li L, Ni B, Qiang Y, et al. Risk assessment of debris flow disaster based on the cloud model—Probability fusion method. Muhammad S, ed. PLOS ONE. 2023; 18(2): e0281039. doi: 10.1371/journal.pone.0281039
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